At our next meeting we took a look at how to build a predictive model based on the Random Forest algorithm.

But boosting parameters to our Random Forest algorithm didn't drastically improve our score. This is when we found out about the Mocha package.

Convolutional Neural Networks

The recent resurgence in popularity of Neural Networks is due the the amazing performance of Convolutional Neural Networks (CNNs) at image classification. This was exactly the solution our problem needed. Dung Thai is very knowledgeable about Deep Learning, and encouraged us to try out the Mocha Package for Deep Learning in Julia. As a result we quickly moved into the top 20 on the leaderboard.

Pulling out all the Stops

At our next meeting Dung (pronounced Yung) summarized Learn from the Best , and we talked about how to get to the next level. Data Science Saigon has talent across a variety of platforms and languages including C++, Caffe, Scilab, Python and of course Julia. We also noticed a few things about the rules for this particular competition:

Saigon là số một.

Then last week Dung Thai, Vinh Vu, and Nguyen Quy checked in Python code using Theano that recognizes over 92% of the images correctly, and vaulted us into the #1 spot on the Getting Started with Julia leaderboard. Congratulations to everyone taking part in Data Science Saigon.

Our Remaining Challenge

So clearly, training with lots more data improved the score. But the question remains, would using a CNN in Julia with the additional training data generate a similar score as the Python code? We hope to find out when we meet again. All of our code is here.

17 comments:

Our score is no long on the leaderboard. Kaggle has implemented a rolling leaderboard: https://www.kaggle.com/c/digit-recognizer/forums/t/6250/rolling-leaderboards/36132 . Just as well, since we used Python code in a Julia competition.

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